Metric details with threshold from accuracy metric
score
threshold
logloss
0.220615
nan
auc
0.943118
nan
f1
0.891007
0
accuracy
0.803483
0
precision
0.803438
0
recall
1
0
mcc
0.030389
0
Confusion matrix (at threshold=0.0)
Predicted as Major
Predicted as Minor
Labeled as Major
1
869
Labeled as Minor
0
3552
Learning curves
Decision Tree
Tree #1
Rules
if (APR Severity of Illness Code <= 2.5) and (Age Group <= 3.5) and (APR Severity of Illness Code > 0.5) then class: Minor (proba: 99.38%) | based on 7,565 samples
if (APR Severity of Illness Code > 2.5) and (APR Severity of Illness Description > 0.5) and (Age Group <= 3.5) then class: Minor (proba: 66.95%) | based on 1,649 samples
if (APR Severity of Illness Code <= 2.5) and (Age Group > 3.5) and (APR Severity of Illness Code > 1.5) then class: Minor (proba: 82.68%) | based on 1,380 samples
if (APR Severity of Illness Code > 2.5) and (APR Severity of Illness Description > 0.5) and (Age Group > 3.5) then class: Major (proba: 74.2%) | based on 1,341 samples
if (APR Severity of Illness Code > 2.5) and (APR Severity of Illness Description <= 0.5) and (APR MDC Description <= 17.5) then class: Major (proba: 96.08%) | based on 739 samples
if (APR Severity of Illness Code <= 2.5) and (Age Group > 3.5) and (APR Severity of Illness Code <= 1.5) then class: Minor (proba: 97.65%) | based on 510 samples
if (APR Severity of Illness Code > 2.5) and (APR Severity of Illness Description <= 0.5) and (APR MDC Description > 17.5) then class: Major (proba: 77.5%) | based on 80 samples
if (APR Severity of Illness Code <= 2.5) and (Age Group <= 3.5) and (APR Severity of Illness Code <= 0.5) then class: Major (proba: 100.0%) | based on 1 samples